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            <article class="content wrap" id="_content" data-uid="Keras.Initializer">
  
  <h1 id="Keras_Initializer" data-uid="Keras.Initializer" class="text-break">Namespace Keras.Initializer
  </h1>
  <div class="markdown level0 summary"></div>
  <div class="markdown level0 conceptual"></div>
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    <h3 id="classes">Classes
  </h3>
      <h4><a class="xref" href="Keras.Initializer.Constant.html">Constant</a></h4>
      <section><p>Initializer that generates tensors initialized to a constant value.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.GlorotNormal.html">GlorotNormal</a></h4>
      <section><p>Glorot normal initializer, also called Xavier normal initializer.
It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.GlorotUniform.html">GlorotUniform</a></h4>
      <section><p>Glorot uniform initializer, also called Xavier uniform initializer.
It draws samples from a uniform distribution within[-limit, limit] where limit is sqrt(6 / (fan_in + fan_out)) where fan_in is the number of input units in the weight tensor and fan_out is the number of output units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.HeNormal.html">HeNormal</a></h4>
      <section><p>He normal initializer.
It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(2 / fan_in) where fan_in is the number of input units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.HeUniform.html">HeUniform</a></h4>
      <section><p>He uniform variance scaling initializer.
It draws samples from a uniform distribution within[-limit, limit] where limit is sqrt(6 / fan_in) where fan_in is the number of input units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.Identity.html">Identity</a></h4>
      <section><p>Initializer that generates the identity matrix.
Only use for 2D matrices.If the desired matrix is not square, it pads with zeros on the additional rows/columns</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.LecunNormal.html">LecunNormal</a></h4>
      <section><p>LeCun normal initializer.
It draws samples from a truncated normal distribution centered on 0 with stddev = sqrt(1 / fan_in) where fan_in is the number of input units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.LecunUniform.html">LecunUniform</a></h4>
      <section><p>LeCun uniform initializer.    It draws samples from a uniform distribution within[-limit, limit] where limit is sqrt(3 / fan_in) where fan_in is the number of input units in the weight tensor.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.Ones.html">Ones</a></h4>
      <section><p>Initializer that generates tensors initialized to 1.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.Orthogonal.html">Orthogonal</a></h4>
      <section><p>Initializer that generates a random orthogonal matrix.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.RandomNormal.html">RandomNormal</a></h4>
      <section><p>Initializer that generates tensors with a normal distribution.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.RandomUniform.html">RandomUniform</a></h4>
      <section><p>Initializer that generates tensors with a uniform distribution.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.TruncatedNormal.html">TruncatedNormal</a></h4>
      <section><p>Initializer that generates a truncated normal distribution.
These values are similar to values from a RandomNormal except that values more than two standard deviations from the mean are discarded and redrawn.This is the recommended initializer for neural network weights and filters.</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.VarianceScaling.html">VarianceScaling</a></h4>
      <section><p>Initializer capable of adapting its scale to the shape of weights.
With distribution = &quot;normal&quot;, samples are drawn from a truncated normal distribution centered on zero, with stddev = sqrt(scale / n) where n is:
number of input units in the weight tensor, if mode = &quot;fan_in&quot;,
number of output units, if mode = &quot;fan_out&quot;,
average of the numbers of input and output units, if mode = &quot;fan_avg&quot;,
With distribution = &quot;uniform&quot;, samples are drawn from a uniform distribution within[-limit, limit], with limit = sqrt(3 * scale / n).</p>
</section>
      <h4><a class="xref" href="Keras.Initializer.Zeros.html">Zeros</a></h4>
      <section><p>Initializer that generates tensors initialized to 0.</p>
</section>
</article>
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